[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-83230-en":3,"doc-seo-83230-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},83230,962075114765,"Quinn","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Generalist Vision Language Models for Fast Radio Burst detection: a zero-shot benchmark against a specialized detector","Fast Radio Bursts (FRBs) are millisecond radio transients whose automated detection typically depends on highly specialized deep learning systems trained on large, task-specific datasets. This study tests small open-weight generalist Vision-Language Models (VLMs) in a zero-shot, prompt-only setting to detect FRBs in dynamic spectra without fine-tuning or labeled examples. Using 3000 simulated L-band spectra, benchmarked with structured RFI, noise, and FRBs, the models are compared sample-by-sample with SwinYNet and evaluated under rewritten prompting for multi-class classification.","arXiv :2607 .07382v 1 [ cs .LG] 8 Jul 2026  \nGeneralist Vision-Language Models for Fast Radio Burst detection: a zero-shot  \nbenchmark against a specialized detector  \nRaiff H. Santos ,∗ Amilcar R. Queiroz ,† [K. E. L. de](K. E. L. de) Farias ,‡ and Rafael A. Batista §  \nUniversidade Federal de Campina Grande,  \nR. Apr´ıgio Veloso 882, Bairro Universit´ario,  \n58429-900, Campina Grande, PB, Brazil  \nTharcisyo S. S. Duarte ¶  \nInstituto de Forma¸c˜ao de Educadores, Universidade Federal do Cariri, Rua Oleg´ario Em´ıdio de Ara´ujo, s/n, Aldeota, 63260-000, Brejo Santo, CE, Brazil and Universidade Federal de Campina Grande,  \nR. Apr´ıgio Veloso 882, Bairro Universit´ario,  \n58429-900, Campina Grande, PB, Brazil  \n(Dated: July 9, 2026)  \nFast Radio Bursts (FRBs) are millisecond-duration radio transients whose automated detection increasingly relies on highly specialized deep learning models. These detectors achieve exceptional performance, but they require large task-specific training datasets and cannot be redefined without retraining. In this work, we evaluate whether small, open-weight, locally run generalist VisionLanguage Models (VLMs) can detect FRBs in dynamic spectra under a zero-shot, prompt-only regime, with no fine-tuning and no labeled examples, returning structured decisions with a naturallanguage justification. From a controlled set of 3000 simulated L-band dynamic spectra containing FRBs, structured Radio Frequency Interference (RFI), and noise, we draw a balanced binary benchmark of 2000 samples and compare two such VLMs (Gemma 4 2B and 4B), sample by sample, against the state-of-the-art specialized detector SwinYNet. At the default threshold, Gemma 4 2Breaches an accuracy of 93.65%, with no statistically significant difference from SwinYNet (92.90%), while showing a significantly lower false-positive rate on structured RFI (6.4% vs. 25.0%) and no false positives on pure noise. SwinYNet retains a perfect probabilistic ranking on this benchmark (ROC-AUC of 1.0000 vs. 0.9482), a ceiling that the zero-shot VLM approaches from general-purpose pretraining alone. Rewriting the prompt alone reconfigures the same models for three-class FRB/RFI/noise classification on the full set of 3000 spectra, where they reach up to 86% accuracy without a single false FRB.  \nI. INTRODUCTION  \nFast Radio Bursts (FRBs) are intense radio transients with durations of only a few milliseconds and predominantly of extragalactic origin. Since the discovery of the first FRB by [1], these events have become one of the most intriguing topics in modern astrophysics and cosmology [2–7] . FRBs are characterized by their large dispersion measures (DMs) [3, 8, 9], which generally exceed the expected contribution from the Milky Way, indicating propagation through the ionized intergalactic medium (IGM) . Over the past years, hundreds of FRBs have been detected by several radio observatories, including the Parkes telescope, CHIME [10], ASKAP [11], FAST [12], and MeerKAT [13], revealing a diverse population of repeating and apparently non-repeating sources. Because of their extragalactic characteristics, they have recently been used for cosmological probes in several scenarios [14–20] .  \n∗ [raiffhugos@gmail.com](raiffhugos@gmail.com)[ ](raiffhugos@gmail.com)† [amilcarq@df.ufcg.edu.br](amilcarq@df.ufcg.edu.br)[ ](amilcarq@df.ufcg.edu.br)‡ [klecio.lima@uaf.ufcg.edu.br](klecio.lima@uaf.ufcg.edu.br)  \n§ [rafael.batista@uaf.ufcg.edu.br](rafael.batista@uaf.ufcg.edu.br)[ ](rafael.batista@uaf.ufcg.edu.br)¶ [tharcisyo.duarte@ufca.edu.br](tharcisyo.duarte@ufca.edu.br)  \nProcessing these streams in real-time requires automated detection pipelines. Traditional pipelines rely on matched filtering and dedispersion, which are computationally expensive [6, 21–23] . Historically, this process was dominated by CPU-based software packages like PRESTO (PulsaR Exploration and Search TOolkit) [24, 25], which established the foundational algorithms for incoherent dedispersion [2","cbCaitchMbCBXxZd","https://ap.wps.com/l/cbCaitchMbCBXxZd","pdf",1061360,1,24,"English","en",105,"# Introduction\n## FRBs and automated detection pipelines\n## Visual and multimodal deep learning approaches\n## Motivation for zero-shot generalist VLMs","[{\"question\":\"What is the main goal of the document’s study?\",\"answer\":\"Evaluate whether generalist, open-weight Vision-Language Models can detect fast radio bursts in dynamic spectra under a zero-shot, prompt-only regime, without fine-tuning or labeled examples.\"},{\"question\":\"How is the benchmark for FRB detection constructed?\",\"answer\":\"From 3000 simulated L-band dynamic spectra containing FRBs, structured Radio Frequency Interference (RFI), and noise, a balanced binary benchmark of 2000 samples is created for comparison, and additional prompt rewriting enables three-class classification across the full set.\"},{\"question\":\"How do the generalist VLMs perform compared with the specialized detector SwinYNet?\",\"answer\":\"At the default threshold, Gemma 4 2B reaches 93.65% accuracy versus SwinYNet’s 92.90%, with a lower false-positive rate on structured RFI and no false positives on pure noise; however, SwinYNet maintains perfect ranking quality (ROC-AUC 1.0000).\"}]",1784186092,60,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"generalist-vision-language-models-for-fast-radio-burst-detection-a-zero-shot-benchmark-against-a-specialized-detector","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/generalist-vision-language-models-for-fast-radio-burst-detection-a-zero-shot-benchmark-against-a-specialized-detector/83230/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-16",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What is the main goal of the document’s study?","Question",{"text":74,"@type":75},"Evaluate whether generalist, open-weight Vision-Language Models can detect fast radio bursts in dynamic spectra under a zero-shot, prompt-only regime, without fine-tuning or labeled examples.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How is the benchmark for FRB detection constructed?",{"text":79,"@type":75},"From 3000 simulated L-band dynamic spectra containing FRBs, structured Radio Frequency Interference (RFI), and noise, a balanced binary benchmark of 2000 samples is created for comparison, and additional prompt rewriting enables three-class classification across the full set.",{"name":81,"@type":72,"acceptedAnswer":82},"How do the generalist VLMs perform compared with the specialized detector SwinYNet?",{"text":83,"@type":75},"At the default threshold, Gemma 4 2B reaches 93.65% accuracy versus SwinYNet’s 92.90%, with a lower false-positive rate on structured RFI and no false positives on pure noise; 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